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Variable-speed pump power storage is an innovative large-scale technology that is being deployed across the world. In addition to price arbitrage and provision of downward replacement reserve, its operational flexibility enables the provision of frequency restoration reserve (FRR) both in turbine and pump modes. This work proposes a bidding optimization strategy for the participation in the FRR market. The proposed framework encompasses a medium-term module to optimally allocate the yearly natural water inflows, representation of electrical and hydraulic losses in the water inflow/power curves, as well as forecasting techniques to predict market prices and natural water inflows. Moreover, it does not assume prior knowledge of the amount of activated FRR capacity band. An evaluation module is also proposed to replicate the “real” operation of the power plant and enables an accurate calculation of the revenue. A comparison between fixed and variable-speed Pump storage power (PSP) units participating in the Iberian electricity market presented an increase in revenue of almost 12%. Due to the low liquidity of the FRR market in Portugal, and the considerable capacity of the PSP unit, under some specific situations, it might be necessary to cap the size of the FRR bid to decrease the difference between the expected and realized revenue.

Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.

Urban wastewater sector is being pushed to optimize processes in order to reduce energy consumption without compromising its quality standards. Energy costs can represent a significant share of the global operational costs (between 50% and 60%) in an intensive energy consumer. Pumping is the largest consumer of electrical energy in a wastewater treatment plant. Thus, the optimal control of pump units can help the utilities to decrease operational costs. This work describes an innovative predictive control policy for wastewater variable-frequency pumps that minimize electrical energy consumption, considering uncertainty forecasts for wastewater intake rate and information collected by sensors accessible through the Supervisory Control and Data Acquisition system. The proposed control method combines statistical learning (regression and predictive models) and deep reinforcement learning (Proximal Policy Optimization). The following main original contributions are produced: (i) model-free and data-driven predictive control; (ii) control philosophy focused on operating the tank with a variable wastewater set-point level; (iii) use of supervised learning to generate synthetic data for pre-training the reinforcement learning policy, without the need to physically interact with the system. The results for a real case-study during 90 days show a 16.7% decrease in electrical energy consumption while still achieving a 97% reduction in the number of alarms (tank level above 7.2 m) when compared with the current operating scenario (operating with a fixed set-point level). The numerical analysis showed that the proposed data-driven method is able to explore the trade-off between number of alarms and consumption minimization, offering different options to decision-makers.

Several countries have a significant installed capacity of large-scale reversible hydro power plants. This large-scale storage technology comes with high investments costs, hence the constant search for methods to increase and diversify the sources of revenue. Traditional fixed speed pump storage units typically operate in the day-ahead market to perform price arbitrage and, in specific cases, provide downward replacement reserve (RR). Variable speed pump storage can not only participate in RR but also contribute to frequency restoration reserve (FRR), given their ability to control its operating point in pumping mode. This work proposes a strategy to manage the water resource and maximize the power plant revenue by participating in the day-ahead market but also providing ancillary services. Moreover, a model to correctly allocate the water resource throughout the year is presented, as well as an evaluation module to calculate the real revenue of the system.

Photovoltaic (PV) solar power capacity is growing in several countries, either concentrated in medium/large size solar parks or distributed by the medium and low voltage grid. Solar power forecasting is a key input for supporting grid management, participation in the electricity market and maintenance planning. This paper proposes a new forecasting system that is a hybrid of different models, such as electrical and statistical models, as well as different Numerical Weather Prediction (NWP) sources. The time horizon is 48 hours ahead. The proposed model was operationalized and tested in a real PV installation located in North Portugal with 16 kW.

This paper provides an overview of the expected role that variable speed hydro power plants can have in future electric power systems characterized by a massive integration of highly variable sources. Therefore, it is discussed the development of a methodology for optimising the operation of hydropower plants under increasing contribution from new renewable energy sources, addressing the participation of a hydropower plant with variable speed pumping in reserve markets. Complementarily, it is also discussed the active role variable speed generators can have in the provision of advanced frequency regulation services.

The increase penetration of renewable energy sources (RES) into the European power system has introduced a significant amount of variability and uncertainty in the generation profiles raising the needs for ancillary services as well as other tools like demand response, improved generation forecasting techniques and changes to the market design. While RES is able to replace energy produced by the traditional centralized generation, it cannot displace its capacity in terms of ancillary services provided. Therefore, centralized generation capacity must be retained to perform this function leading to over-capacity issues and underutilisation of the assets. Large-scale reversible hydro power plants represent the majority of the storage solution installed in the power system. This technology comes with high investments costs, hence the constant search for methods to increase and diversify the sources of revenue. Traditional fixed speed pump storage units typically operate in the day-ahead market to perform price arbitrage and, in some specific cases, provide downward replacement reserve (RR). Variable speed pump storage can not only participate in RR but also contribute to FRR, given their ability to control its operating point in pumping mode. This work does an extended analysis of a complete bidding strategy for Pumped Storage Power, enhancing the economic advantages of variable speed pump units in comparison with fixed ones.